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1

Artur, Raimundo Fonseca da Silva, Henrique Michels de Sant´Anna Carlos, Daniel Santana de Souza Diogo, Wellington da Silva Junior Odacy, Rayana Bezerra de Lima Rebeca, and Henrique Ildefonso de Souza Thiago. "O IMPACTO DA INTELIGÊNCIA ARTIFICIAL NA EFICIÊNCIA OPERACIONAL DAS ORGANIZAÇÕES." Revistaft 28, no. 133 (2024): 33. https://doi.org/10.5281/zenodo.11111333.

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A ascensão da Inteligência Artificial (IA) está redefinindo os paradigmas da eficiência  operacional nas organizações. Este artigo busca a influência multifacetada da IA  destacando como a automação inteligente, análise avançada de dados e algoritmos  de aprendizado de máquina convergem para transformar a maneira como as  empresas operam. Não se trata apenas de automatizar tarefas, mas sim de uma  revolução na concepção, execução e refinamento dos processos ao longo do tempo.  Enquanto a IA promete inúmeros benefícios, também traz consigo desafios éticos,  técnicos e organizacionais que exigem atenção cuidadosa. Desde questões  relacionadas à privacidade de dados até a resistência à mudança por parte dos  colaboradores, as organizações enfrentam uma série de obstáculos na  implementação bem-sucedida da IA. No entanto, ao abordar esses desafios de  maneira proativa e adotar uma abordagem ética e transparente, as empresas podem  aproveitar todo o potencial transformador da IA para impulsionar a eficiência  operacional e garantir um futuro sustentável e inovador. Além dos desafios mencionados, outro aspecto crucial a ser considerado é o impacto  da IA na força de trabalho. À medida que a automação inteligente se torna mais  difundida, surgem preocupações sobre o futuro do emprego e a necessidade de  requalificação da mão de obra. As organizações enfrentam o dilema de equilibrar a  eficiência operacional proporcionada pela IA com a responsabilidade social de garantir  oportunidades de trabalho significativas para os indivíduos afetados pela automação.  Portanto, a implementação bem-sucedida da IA requer uma abordagem holística que  leve em consideração não apenas os aspectos técnicos, mas também os impactos  sociais e econômicos. Por outro lado, a IA oferece oportunidades sem precedentes  para a inovação e o crescimento empresarial. Ao integrar sistemas inteligentes em  suas operações, as organizações podem identificar padrões ocultos nos dados, prever  tendências de mercado e desenvolver estratégias mais eficazes de tomada de  decisão. Isso não apenas impulsiona a eficiência operacional, mas também abre  novos horizontes para a criação de produtos e serviços inovadores.  
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Panesar, Sandip S., Michel Kliot, Rob Parrish, Juan Fernandez-Miranda, Yvonne Cagle, and Gavin W. Britz. "Promises and Perils of Artificial Intelligence in Neurosurgery." Neurosurgery 87, no. 1 (2019): 33–44. http://dx.doi.org/10.1093/neuros/nyz471.

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Abstract Artificial intelligence (AI)-facilitated clinical automation is expected to become increasingly prevalent in the near future. AI techniques may permit rapid and detailed analysis of the large quantities of clinical data generated in modern healthcare settings, at a level that is otherwise impossible by humans. Subsequently, AI may enhance clinical practice by pushing the limits of diagnostics, clinical decision making, and prognostication. Moreover, if combined with surgical robotics and other surgical adjuncts such as image guidance, AI may find its way into the operating room and permit more accurate interventions, with fewer errors. Despite the considerable hype surrounding the impending medical AI revolution, little has been written about potential downsides to increasing clinical automation. These may include both direct and indirect consequences. Directly, faulty, inadequately trained, or poorly understood algorithms may produce erroneous results, which may have wide-scale impact. Indirectly, increasing use of automation may exacerbate de-skilling of human physicians due to over-reliance, poor understanding, overconfidence, and lack of necessary vigilance of an automated clinical workflow. Many of these negative phenomena have already been witnessed in other industries that have already undergone, or are undergoing “automation revolutions,” namely commercial aviation and the automotive industry. This narrative review explores the potential benefits and consequences of the anticipated medical AI revolution from a neurosurgical perspective.
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Oluwasegun Julius Aroba, Michael Rudolph, Nalindren Naicker, et al. "A Bibliometric Analysis Review: The Emerging Technology of Artificial Intelligence for Non-Bio Inspired and Bio-Inspired Algorithm of Wireless Sensor Network from 2005–2022." International Journal of Computer Information Systems and Industrial Management Applications 17 (February 25, 2025): 21. https://doi.org/10.70917/ijcisim-2025-0015.

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Rapid developments in technology, business, and social norms have been observed in the twenty-first century. The fourth industrial revolution has been brought about by most industries moving toward automation and reducing human intervention. Wireless sensor networks are incredibly important to the fourth industrial revolution since they help with modernization. WSNs are networks of sensor and routing nodes that can be integrated into a variety of control systems, such as those used for home automation, electric-power automation, and environmental monitoring. A key problem that typically afflicts wireless sensor networks is node localization (WSNs). As a result, several algorithms, to ameliorate the challenges WSNs confront, both bio-inspired and non-bio-inspired solutions have been presented. From 2005 through 2022, the Scopus database was searched for publications. WSNs are used in published research paper statistical analysis, Microsoft Excel 365, VOSviewer, RStudio, and Biblioshiny packages were used. For this seventeen-year study period, a total of 36,377 published documents were in the Scopus database. 765 papers in all were examined following the implementation of the exclusion criteria. This study highlights the global research production of bio-inspired and non-bioinspired algorithms in wireless sensor networks, together with their status and tendencies. It can assist IoT and wireless sensor network researchers in gaining a thorough understanding of the most advanced algorithms in this area.
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Gunawan, Ali. "Robotic Processes Automation to Improve Business Process Automation: A Systematic Literature Reviews." E3S Web of Conferences 426 (2023): 01009. http://dx.doi.org/10.1051/e3sconf/202342601009.

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Technological developments have been very fast since 2000 until now, and there are many information system applications in all corners of the world. Now, many services to customers or the public are provided by companies, from small to enterprise software, and many institutions already use the platform— digital services to serve customers and society. Industry 4.0 is the fourth industrial revolution since 2010, where technology and accelerated processes have used automation and are accelerating. This technology is Robotic Process Automation (R.P.A) which could automate many organizational business processes and use Artificial Intelligence (AI) algorithms and techniques. Complementarity makes it possible to increase the accuracy, efficiency, effectiveness, and implementation of automation processes in business processes, in recognition, classification, forecasting, and process optimization. This study aims to provide research information on the application of R.P.A related to AI, which can contribute to improving organizational processes related to Industry 4.0. Researchers found that automation with R.P.A has been widely applied in various industries, various implementations of R.P.A Technology have been carried out, and many publishers have published on this topic.
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Venkata Amarnath Rayudu Amisetty. "AI-driven mobile and web automation: The CI/CD integration revolution." World Journal of Advanced Research and Reviews 26, no. 2 (2025): 3554–62. https://doi.org/10.30574/wjarr.2025.26.2.2039.

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Artificial intelligence has fundamentally transformed mobile and web automation practices when integrated with modern CI/CD pipelines, creating unprecedented efficiency gains throughout the software development lifecycle. This technical article examines cutting-edge advancements in self-healing test frameworks powered by neural networks that autonomously repair broken test scripts while maintaining exceptional recognition rates across dynamically changing interfaces. The integration of Jenkins within cloud environments enables remarkable scalability improvements through containerized infrastructures, allowing organizations to dramatically reduce test execution time and accelerate deployment cycles. Leading automation frameworks like Testim, Appium, and Functionize leverage sophisticated machine learning algorithms to enhance test stability, enable cross-platform compatibility, and provide autonomous test maintenance. Implementation strategies focusing on hybrid framework adoption, containerized test environments, progressive testing rollouts, and continuous model refinement yield substantial benefits across enterprise organizations. Despite technical challenges involving training data requirements, pipeline scalability, result interpretation, and cross-platform consistency, effective solutions have emerged to address these barriers. Future directions point toward zero-code test generation, predictive quality assurance, self-optimizing pipelines, and federated learning networks.
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Da Costa-Abreu, Márjory, and Bruno dos Santos F. Silva. "A critical analysis of ’Law 4.0’: The use of Automation and Artificial Intelligence and their impact on the judicial landscape of Brazil." Revista de Direitos Fundamentais e Tributação 1, no. 3 (2020): 01–16. http://dx.doi.org/10.47319/rdft.v1i3.30.

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There is a digital revolution, called Industry 4.0, happening around the world(and therefore, in Brazil as well!) that is shifting our activities from an ’analogic’ to a ’digital’ format. From health to education, we can see more and morethe digitalisation and the automation taking a key part of the work involved inmanaging data (being it private or public) and optimising the processes in general. With this move, the ’realisation’ that there are several possible ways toperform automation including the use of intelligent systems came to light andit has become a particular favourite term used in any situation to name anycomputational system. In the justice area, it has not been different, and, particularly, in the Brazilian Justice system, there is a strong move to have as muchautomation, digitalisation of the processes as possible. However, the generalunderstanding of what algorithms, automation and intelligent systems can beor perform are very foggy and, more often than not, we can see the word ’intelligent’ being used inadvertently. Thus, this paper will aim at simply definethe keywords from the computer science area: algorithm, automation and intelligent systems (artificial intelligence), evaluate the systems that are in use inthe Brazilian Justice System, specifically indicating in which category they falland, finally, discuss the impact of using intelligent systems without any humanintervention in the context of the so called ’Law 4.0’.
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Xue, Yike. "Optimisation of Automation Devices Based on IOT Big Data Algorithms." BIO Web of Conferences 72 (2023): 02003. http://dx.doi.org/10.1051/bioconf/20237202003.

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With the continuous development of communication, sensor and caching technologies, IoT technology has gained rapid opportunities for growth and a huge digital revolution has taken place at all levels of society. Blockchain technology has emerged rapidly in recent years and can be seen as a distributed, time-based ledger of distributed data. It utilises technologies such as consensus protocols, modern cryptography, P2P and smart contracts, which can provide a secure, stable, transparent, auditable and low-consumption system architecture that has a traceable, stable and efficient security management capability, and can provide a new solution to identity security for the Internet of Things. This paper absorbs the existing blockchain-based access management approach and improves it, proposing a new private chain-based security management approach that solves the problems of access dynamics, low intelligence and high overhead in the traditional access management approach. This paper designs a new control management architecture, the Novel-Capability-Based Access Control (NCBAC), which draws on the microkernel and microservice ideas of operating systems. Firstly, this paper abstracts the concept of management node to solve the problem of weak computing power and low storage performance of IoT devices that cannot meet the difficulty of direct communication between IoT devices and blockchain, and at the same time can reduce the network operation overhead; secondly, it constructs a multi-level smart contract system and designs three kinds of smart contracts, AC, ACC and AMC, to build a trusted and reliable access control entity model; finally, it adopts radial basis based (RBF) neural network and combines with access policy to dynamically generate the credit degree threshold of access nodes to build an intelligent access authority management model for IoT mass sensors. The model proposed in this paper designs a token mechanism based on the fact that IoT systems have multiple requests within a short period of time in a real production environment, which, according to experimental results, improves the performance of the system to a certain extent.
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Heruatmadja, Chandra Hermawan, Harjanto Prabowo, H. Leslie Hendric Spits Warnar, and Yaya Heryadi. "Warehouse Technology Revolution: Integration of Drones and Deep Learning Algorithms for Stock Identification and Calculation Automation." Journal of Advanced Research in Applied Sciences and Engineering Technology 64, no. 4 (2025): 74–90. https://doi.org/10.37934/araset.64.4.7490.

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The technological revolution in warehouse management is accelerating, especially with the use of drones and deep learning algorithms to overcome operational challenges. This research aims to develop a deep learning-based automation system that integrates drones for accurate and efficient identification and inventory calculation in retail warehouses. The research method uses the Design Science Research Methodology (DSRM) approach, which includes problem identification, solution development, demonstration, evaluation, and communication. The dataset contains more than 4,000 images of annotated goods and is used to train YOLO, Mask R-CNN, and RetinaNet models. The evaluation was conducted using Precision, Recall, and Average Precision (mAP) metrics. The test results show that YOLO has the best performance with an average mAP of 0.5 of 0.978 and a processing time of only 4.82 seconds, far superior to Mask R-CNN and RetinaNet. In addition, drone integration allows for efficient imaging of goods, reduces the risk of work accidents, and improves inventory accuracy by up to 100%. The results of this study highlight the importantance of combining drone technology and deep learning algorithms to create reliable solutions in inventory management, especially in dynamic retail environments. This research makes a significant contribution to the optimization of logistics processes and warehouse operations, as well as being the basis for further development in the industry.
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9

Shafeeq, Ur Rahaman. "Beyond the Data Lake: Harnessing Real-Time Analytics and Automation for Dynamic Decision-Making." International Journal of Innovative Research in Engineering & Multidisciplinary Physical Sciences 75, no. 5 (2019): 1–9. https://doi.org/10.5281/zenodo.14352006.

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The revolution going from traditional static data lakes to agile, real-time analytics engines that is really changing how organizations derive value from their data. Advanced integration of real-time analytics with automation in dynamic decision-making is discussed in this article. Using real-time streaming of data, machine learning algorithms, and intelligent automation, the raw data transformation into actionable insights can be facilitated for organizations in real time. These are the very latest innovations that enable companies to make business operations more efficient, significantly enhance customer experiences, and gain a competitive advantage over their rivals. Real case studies from different industries showcase how this shift toward real-time analytics works in reality and what benefits it presents. Furthermore, this paper discusses various challenges when transitioning to real-time analytics in the realms of data governance, scalability, and cost consideration, with proposition strategies to help resolve these issues. The findings came quite the opposite from expectations, yet they point to a critical role of real-time analytics and automation, shaping the future of data-driven decision-making
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Bindushree G T and Sreedevi M T. "Review on poultry automation using IoT and machine learning." World Journal of Advanced Research and Reviews 8, no. 3 (2020): 466–74. https://doi.org/10.30574/wjarr.2020.8.3.0371.

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The poultry industry is undergoing a transformative technological revolution through the strategic integration of Internet of Things (IoT) and Machine Learning (ML) technologies. This comprehensive review explores the current state of poultry automation, examining innovative approaches that leverage advanced sensor networks, data analytics, and intelligent algorithms to address critical challenges in agricultural productivity. By synthesizing emerging research, the paper demonstrates how IoT and ML technologies are revolutionizing poultry farming through enhanced environmental monitoring, predictive health management, and precision resource optimization. These technological interventions offer unprecedented insights into animal welfare, operational efficiency, and sustainable farming practices, enabling real-time tracking of physiological parameters, early disease detection, and intelligent decision-making frameworks. The review critically analyzes the potential of these technologies to transform traditional agricultural methods, highlighting their capacity to improve farm productivity, reduce manual labor, and implement more targeted and responsive management strategies. As global food security demands increasingly sophisticated agricultural solutions, the convergence of IoT and ML represents a pivotal advancement in creating more intelligent, efficient, and responsive poultry production systems.
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Feng, Shuai. "Toward a Transformed and Unequal World." China Quarterly of International Strategic Studies 05, no. 02 (2019): 267–87. http://dx.doi.org/10.1142/s2377740019500118.

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With the increasing use of machine learning algorithms in strategic decision-making and military affairs, the artificial intelligence (AI) revolution is bringing about significant changes to the current international system. AI applications will further tilt the global balance of power in favor of actors who can make the best use of the emerging technology. AI-assisted automation is also changing prevailing socioeconomic production models on the global scale; and in the not too distant future, it is expected to exert systemic impacts on the current global order. Having recognized the full potential of AI technology in propelling the next industrial revolution, China has adopted an AI development strategy for guiding a nation-wide campaign to harness AI power at an early stage, so as to seize the strategic initiative in an emerging global AI competition. China’s initial efforts have produced remarkable achievements with theoretical and practical implications.
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Abraham, Saju T., Manju Mohan, Pandian Chelliah, Krishnan Balasubramaniam, and B. Venkatraman. "A machine learning approach to nonlinear ultrasonics for classifying annealing conditions in austenitic stainless steel." Journal of Applied Physics 132, no. 11 (2022): 114902. http://dx.doi.org/10.1063/5.0102337.

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This paper explores the feasibility of machine learning algorithms on nonlinear ultrasonics for classification of the austenitic stainless-steel material subjected to different annealing conditions. The material that is isothermally annealed at 1323 K for different soaking times showed a marginal variation in its nonlinearity parameter at larger mean grain sizes. The grain growth during annealing followed the Arrhenius type equation fairly well, which has been verified using a genetic algorithm approach. The machine learning algorithms are trained using features such as the ratio of the harmonic amplitudes, root-mean-square value, and the phase difference between the fundamental and second harmonic components derived from the nonlinear ultrasonic response. Upon evaluating the performance of decision tree and ensemble learning algorithms in the classification of annealed materials, it was observed that the LPBoost classifier has the highest accuracy of 97%. According to the results, it is concluded that a machine learning strategy based on a minimal number of features can effectively classify specimens that are otherwise indistinguishable in their nonlinear response. This research takes a step forward to the automation of non-destructive testing toward Industrial Revolution 4.0. The results also pointed out the necessity of parameter fusion in non-destructive decision making.
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BORISOVA, Angela R., and Linko G. NIKOLOV. "SYSTEMS WITH ARTIFICIAL INTELLIGENCE FOR DEFENSE AND SECURITY." SCIENTIFIC RESEARCH AND EDUCATION IN THE AIR FORCE 24 (July 28, 2023): 53–58. http://dx.doi.org/10.19062/2247-3173.2023.24.7.

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To date, artificial intelligence systems are predicted to rapidly enter the defense and security sector. The importance of such automation systems is still under research, in order the qualitative and qualitative indicators for decision making to be improved. In such researches, real-time data analysis tools have to be used and their data exchanged. Common examples are surveillance drones, large amount of sensors and the connection to cloud computing. In the field of cybersecurity AI algorithms undergo continuous revolution. By the use of Artificial intelligence algorithms predictions are the key results for adequate and correct decisions. Huge amounts of data processing define the possibility to make right decisions in non-deterministic environments. In this review report an overview of the already used systems is made and benefits and risks of using artificial intelligence systems in defense and security is examined.
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Anagnoste, Sorin. "Robotic Automation Process - The next major revolution in terms of back office operations improvement." Proceedings of the International Conference on Business Excellence 11, no. 1 (2017): 676–86. http://dx.doi.org/10.1515/picbe-2017-0072.

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Abstract Forced to provide results consistent results to shareholders the organizations turned to Robotic Process Automation (RPA) in order to tackle the following typical challenges they face: (1) Cost reduction, (2) Quality increase and (3) Faster processes. RPA is now considered the next big thing for the Shared Services Centers (SSC) and Business Process Outsourced (BPO) around the world, and especially in Central and Eastern Europe. In SSCs and BPOs the activities with the highest potential for automation are in finance, supply chain and in human resource departments. This means that the problems these business are facing are mostly related to high data entry volumes, high error rates, significant rework, numerous manual processes, multiple not-integrated legacy systems and high turnover due to repetitive/low value added activities. One advantage of RPA is that it can be trained by the users to undertake structured repeatable, computer based tasks interacting in the same time with multiple systems while performing complex decisions based on algorithms. By doing this, the robot can identify the exceptions for manual processing, remove idle times and keep logs of actions performed. Another advantage is that the automated solutions can work 24/7, it can be implemented fast, work with the existing architecture, cut data entry costs by up to 70% and perform at 30% of the cost of a full time employee, thus providing a quick and tangible return to organizations. For Romania, a key destination for SSCs and BPOs, this technology will make them more competitive, but also will lead to a creation of a series of high-paid jobs while eliminating the low-input jobs. The paper will analyze also the most important vendor providers of RPA solutions on the market and will provide specific case studies from different industries, thus helping future leaders and organizations taking better decisions.
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Kodytek, Pavel, Alexandra Bodzas, and Jan Zidek. "Automated code development based on genetic programming in graphical programming language: A pilot study." PLOS ONE 19, no. 3 (2024): e0299456. http://dx.doi.org/10.1371/journal.pone.0299456.

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Continual technological advances associated with the recent automation revolution have tremendously increased the impact of computer technology in the industry. Software development and testing are time-consuming processes, and the current market faces a lack of specialized experts. Introducing automation to this field could, therefore, improve software engineers’ common workflow and decrease the time to market. Even though many code-generating algorithms have been proposed in textual-based programming languages, to the best of the authors’ knowledge, none of the studies deals with the implementation of such algorithms in graphical programming environments, especially LabVIEW. Due to this fact, the main goal of this study is to conduct a proof-of-concept for a requirement-based automated code-developing system within the graphical programming environment LabVIEW. The proposed framework was evaluated on four basic benchmark problems, encompassing a string model, a numeric model, a boolean model and a mixed-type problem model, which covers fundamental programming scenarios. In all tested cases, the algorithm demonstrated an ability to create satisfying functional and errorless solutions that met all user-defined requirements. Even though the generated programs were burdened with redundant objects and were much more complex compared to programmer-developed codes, this fact has no effect on the code’s execution speed or accuracy. Based on the achieved results, we can conclude that this pilot study not only proved the feasibility and viability of the proposed concept, but also showed promising results in solving linear and binary programming tasks. Furthermore, the results revealed that with further research, this poorly explored field could become a powerful tool not only for application developers but also for non-programmers and low-skilled users.
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Mardoyo, Ega, Muharman Lubis, and Susetyo Bagas Bhaskoro. "Evaluasi Virtual Reality Menggunakan Technology Acceptance Model (TAM) Terkait Dunia Metaverse." Jurnal Sistem Cerdas 5, no. 3 (2022): 182–94. http://dx.doi.org/10.37396/jsc.v5i3.250.

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Abstract - Digital disruption makes innovation and transformational sustainable in the digital world. The Industrial Revolution 4.0 has changed the face of the new digital era with a diverse foundation of new and advanced technologies in it. According to Geissbauer (2016) there are 10 technologies that are the foundation of Industrial Revolution 4.0 i.e. mobile devices, IoT platforms, location detection technologies, advanced human machine interfaces, automation & Fraud Detection, 3D manufacturing, smart sensors, big data analytics & advanced algorithms, Multilever customer interaction and customer profiling, Augmented Reality / wearables / immersive technologies, cloud computing. Virtual Reality as one of the technological foundations in the Industrial Revolution 4.0 has grown rapidly along with the term ‘Metaverse’ echoed by Mark Zuckerberg CEO of Facebook in October 2021. Metaverse itself is identified as a virtual world where all human activities can be done in it. Virtual Reality as one of the components of the Metaverse is an attraction for the industry to start this technology, especially in exhibition activities (Expo). Virtual Reality Expo for SME is one of the important foundations regarding user understanding regarding the introduction of Virtual Reality technology. Therefore, as an advanced technology that is currently rampant, the author conducts research on Virtual Reality Expo using the technology Acceptance Model (TAM).
 
 
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Kumar, B. K. Praveen, and Dr K. Santhi Sree. "Virtualization-Based Digitization of a Retail Store: An Enhanced Implementation of Digital Transformation." International Journal of Innovative Technology and Exploring Engineering 10, no. 11 (2021): 133–36. http://dx.doi.org/10.35940/ijitee.k9488.09101121.

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The rise of neoteric technologies like Machine Learning, the Internet of Things, Cloud Services, etc has affected the life of a common man at various levels. Irrespective of the size or domain, almost all companies are now incorporating digitization to various degrees and thus progressing towards a new business model with little or no significance to geographical and physical barriers. This shift from traditional store models to automated entities is referred to as Digital Transformation. It is the simplified way of outlining how digital technologies are transforming and automating business operations across all organizations irrespective of their domain. This digital revolution relies on a whole range of machinery, networks, services, and operations to expand their power of communication, thus ensuring seamless integration with the technologies. At this juncture, there would be many challenges in both technical and non-technical aspects. To facilitate successful automation by resolving those issues, using the concept of virtualization can be very helpful. Virtualization is the process of creating a virtual instance of hardware resources like virtual applications, servers, or storage by logically separating them from the hardware. It enables multiple applications or operations to gain access to the hardware resources/ software resources of the host machine. In a sense, virtualization is always at the center of all this revolution providing a rock-solid foundation. For example, when digitizing an organization, machine learning algorithms are applied to the IoT data in addition to the organizational data. Given the huge size of data, companies adapting to this automation rely on cloud services for data management because of the reliability it provides. This issue is solved using the Edge Computing concept which is an advanced implementation of Virtualization. In this paper, we try to discuss such challenges and try to understand how virtualization can be useful in solving them. This can be exemplified using a hypothetical digitalization in a retail store scenario.
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B.K, Praveen Kumar, and Santhi Sree K. "Virtualization-Based Digitization of a Retail Store: An Enhanced Implementation of Digital Transformation." International Journal of Innovative Technology and Exploring Engineering (IJITEE) 10, no. 11 (2021): 133–36. https://doi.org/10.35940/ijitee.K9488.09101121.

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The rise of neoteric technologies like Machine Learning, the Internet of Things, Cloud Services, etc has affected the life of a common man at various levels. Irrespective of the size or domain, almost all companies are now incorporating digitization to various degrees and thus progressing towards a new business model with little or no significance to geographical and physical barriers. This shift from traditional store models to automated entities is referred to as Digital Transformation. It is the simplified way of outlining how digital technologies are transforming and automating business operations across all organizations irrespective of their domain. This digital revolution relies on a whole range of machinery, networks, services, and operations to expand their power of communication, thus ensuring seamless integration with the technologies. At this juncture, there would be many challenges in both technical and non-technical aspects. To facilitate successful automation by resolving those issues, using the concept of virtualization can be very helpful. Virtualization is the process of creating a virtual instance of hardware resources like virtual applications, servers, or storage by logically separating them from the hardware. It enables multiple applications or operations to gain access to the hardware resources/ software resources of the host machine. In a sense, virtualization is always at the center of all this revolution providing a rock-solid foundation. For example, when digitizing an organization, machine learning algorithms are applied to the IoT data in addition to the organizational data. Given the huge size of data, companies adapting to this automation rely on cloud services for data management because of the reliability it provides. This issue is solved using the Edge Computing concept which is an advanced implementation of Virtualization. In this paper, we try to discuss such challenges and try to understand how virtualization can be useful in solving them. This can be exemplified using a hypothetical digitalization in a retail store scenario.
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Parida, S., E. Alemdar, and R. R. Poznanski. "‘Quantum of information’ functionality as a measure of subjectivity beyond the capabilities of deep learning." Journal of Multiscale Neuroscience 3, no. 2 (2024): 145–59. http://dx.doi.org/10.56280/1630287704.

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The potential of conscious artificial intelligence (AI), with its functional systems that surpass automation and rely on elements of understanding, is a beacon of hope in the AI revolution. The shift from automation to conscious AI, once replaced with machine understanding, offers a future where AI can comprehend without needing to experience, thereby revolutionizing the field of AI. In this context, the proposed Dynamic Organicity Theory of consciousness (DOT) stands out as a promising and novel approach for building artificial consciousness that is more like the brain with physiological nonlocality and diachronicity of self-referential causal closure. However, deep learning algorithms utilize "black box" techniques such as “dirty hooks” to make the algorithms operational by discovering arbitrary functions from a trained set of dirty data rather than prioritizing models of consciousness that accurately represent intentionality as intentions-in-action. The limitations of the “black box” approach in deep learning algorithms present a significant challenge as quantum information biology, or intrinsic information, is associated with subjective physicalism and cannot be predicted with Turing computation. This paper suggests that deep learning algorithms effectively decode labeled datasets but not dirty data due to unlearnable noise, and encoding intrinsic information is beyond the capabilities of deep learning. New models based on DOT are necessary to decode intrinsic information by understanding meaning and reducing uncertainty. The process of “encoding” entails functional interactions as evolving informational holons, forming informational channels in functionality space of time consciousness. The “quantum of information” functionality is the motivity of (negentropic) action as change in functionality through thermodynamic constraints that reduce informational redundancy (also referred to as intentionality) in informational pathways. It denotes a measure of epistemic subjectivity towards machine understanding beyond the capabilities of deep learning.
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Rosandhi, Tantri Mugi Utami, and Rudi Haryadi. "The Ability of Computational Thinking in Physics Learning." Jurnal Materi dan Pembelajaran Fisika 14, no. 2 (2024): 98. https://doi.org/10.20961/jmpf.v14i2.79742.

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<p>The introduction describes the critical role of computational thinking in the digital era and ecosystem and developments in the global economy, work, and everyday life. Computational thinking skills are critical, especially in the Industrial Revolution 4.0 era, where technology and automation play a central role. Computational thinking includes problem solving, critical thinking, and integrating digital technology with human ideas. Abstraction, decomposition, algorithms, and evaluation are some of the main aspects of computational thinking skills. The research method used is a literature study with content analysis techniques. This research concludes that Computational Thinking is an approach that can improve the quality of physics learning, concept understanding, critical thinking skills, and student learning outcomes. Obstacles can be overcome by collaborating with other learning methods and approaches.</p>
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Pavlović, Hrvoje, Marko Budimir, Fran Milković, et al. "Improvement Possibilities for Nuclear Power Plants Inspections by Adding Deep Learning-based Assistance Algorithms Into a Classic Ultrasound NDE Acquisition and Analysis Software." Journal of Energy - Energija 71, no. 1 (2023): 40–42. http://dx.doi.org/10.37798/2022711410.

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The safety of nuclear power plants has always been one of the most important security issues in the industry in general. Numerous standards, techniques, and tools have been developed to deal specifically with the safety of nuclear power plants – one has specialised probes, robotized systems, electronics, and software. Although seen as a mature (or slowly evolving) industry, this notion about nuclear safety is a bit misleading – the area is developing in many promising new directions. Some recent global events will speed up this development even more. On the other hand, the industry is currently going through digital transformation, which brings networking of devices, equipment, computers, and humans. This fourth industrial revolution promises speed, reliability, and efficiencies not possible up until now. In the NDE sector, new production techniques and traditional manufacturing lines are getting to be lights-out operations (near-total automation). The same is most probably going to happen with the safety inspections and quality insurance. Robotics and automation are improving worker safety and reducing human error. The well-being of inspectors working in a hazardous environment is being taken care of. Most experts agree that the digitalization of NDE offers unprecedented opportunities to the world of inspection for infrastructure safety, inspector well-being, and even product design improvements. While the community tends to agree on the value proposition of digital transformation of NDE, it also recognizes the challenges associated with such a major shift in a well-established and regulated sector. The work presented in this paper shows a part of the project that aims to develop a modular ultrasound diagnostic NDE system (consisting of exchangeable transducers, electronics, and acquisition/analysis software algorithms), for applications in hazardous environments within nuclear power plants. The paper will show how the software part of this system can reach near-total automation by implementing various deep learning algorithms as its features and, then, testing those algorithms on laboratory samples, showing encouraging results and promises of online monitoring applications. Furthermore, future general prospects of this technology are discussed, and how this technology can affect the well-being of nuclear power plant inspectors and contribute to overall plant safety.
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Tiwari, Tanuja, and Pallavi. "The AI-powered Cleanup: A Revolution in Solid Waste Management." International Journal of Environment and Climate Change 15, no. 3 (2025): 481–90. https://doi.org/10.9734/ijecc/2025/v15i34787.

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Managing solid waste is a critical global issue that demands innovative strategies to enhance efficiency, sustainability, and environmental impact. Waste generation varies across sectors and regions, both in quantity and composition, making its management a critical environmental issue. The escalating decline of ecological quality directs the scientific community toward analyzing and optimizing waste management strategies. Artificial Intelligence (AI) has appeared to bring a revolution in this area by improving the processes of waste collection, segregation, recycling, and disposal. Various models and algorithms have been explored and evaluated for their potential to lead to more sustainable solid waste management (SWM) practices. AI-powered systems leverage data analytics, computer vision, machine learning, and automation to reduce landfill problems, lower operational costs, and support the circular economy. Advanced ML technologies, like deep learning and predictive analytics models, are being utilized for route optimization to ensure timely service delivery and adjust collection schedules accordingly. Smart bin systems equipped with sensors, IoT, and machine learning algorithms are enhancing waste collection and disposal efficiency. AI-generated predictive models significantly aid in waste management planning to adapt to changing waste generation patterns. Technologies like GPS and volumetric sensors, provide an encouraging solution to enhance the efficiency of waste collection systems, and waste-sorting robots can greatly improve the accuracy of waste segregation. Sensor-based waste monitoring tracks the amount of generated waste and identifies its sources in a given area. AI-powered surveillance cameras and drones can promptly detect illegal dumping, enabling authorities to respond swiftly. Thus, SWM can be strengthened by utilizing AI technologies in intelligent waste sorting, recycling, and disposal, leading to more sustainable practices. However, despite the efficiency of AI in supporting SWM systems, high cost, inconsistent data quality, traditional mindset, operational difficulties, etc., pose a challenge to their widespread adoption. There is still a notable gap in its practical application and comprehensive evaluation. To bridge this gap, targeted research on cost-effective solutions and real-world pilot projects is crucial, coupled with collaboration among technology developers, policymakers, and waste management professionals. This paper explores how AI can revolutionize waste management, leading to more efficient strategies and a cleaner future.
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Permiashkin, D. A. "On Solving Concurrent Process Problem in Process-Oriented Programs." Vestnik NSU. Series: Information Technologies 21, no. 2 (2023): 5–17. http://dx.doi.org/10.25205/1818-7900-2023-21-2-5-17.

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Prevailing portion of the factories and manufacturers are controlled by programming microcontrollers in the modern world. And the portion keeps growing which is closely tied to processes of the Fourth Industrial Revolution. Precisely, with an idea of fully automated manufactures to help humans to make less decisions and make them faster. Or to exclude humans from the decision making process at all. Due to that, there is a need for the controlling algorithms which should react to the different events, be aware of the external world and be tolerant to both internal and hardware failures. There is a process-oriented paradigm which was developed in Institute of Automation and Electrometry SB RAS and suits perfectly for automatization of such algorithms. This is achieved by splitting the algorithm into huge amounts of the small parallel processes highly tied to the elements of the real world. Which is how real processes on real manufactures work. This is where the conflicts during concurrent programming appear. And because there is a fault tolerance requirement there is a need to solve those conflicts. This work presents the analysis of already existing solutions to the conflicts during concurrent programming with the goal of either reusing those solutions in process-oriented programming or adapting them to it. As a result, there is an answer on how effective the process-oriented paradigm is in solving those kinds of conflicts and how fault tolerant those programs are.
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Karthikeyan, Mani, and Vediappan Selvan. "FPGA Centric Attention Based Deep Learning Network Evoked Chaotic Encryption to Mitigate Side Channel Attacks." Proceedings of the Bulgarian Academy of Sciences 76, no. 6 (2023): 936–45. http://dx.doi.org/10.7546/crabs.2023.06.14.

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Security concerns are growing, especially in applications where sensitive information is sent (such as health care devices, credit cards, and even Internet of Things (IoT) nodes), even while these developments bring in a modern revolution. This is due to the constant scaling of technologies, which has now been incredibly useful for delivering the new trend of development in the numerous domains such as communications, semiconductors, health care, and automation. Even when cutting-edge standard encryption algorithms are used in smart devices, the principal threats, such as Side-Channel Attacks (SCA) and Correlation Power Analysis (CPA), increase the susceptibility of encrypted cipher text to attacks. In order to defend against side channel assaults, the usefulness of deep learning algorithms combined with chaotic principles is examined in this paper. Scroll Mapping (SM) and Attention-Evoked Long Short-Term Memory (AE-LSTM) are used in the proposed study to effectively safeguard sensitive data while consuming little power. The most promising advancements in terms of all security metrics and hardware capabilities for obtaining high-speed performances are shown by experimental findings on an FPGA implementation of a suggested framework.
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Sankar, P., M. Robinson Joel, and A. Jahir Husain. "Design and Implementation of Intelligent Traffic-Management System for Smart Cities using Roaming Agent and Deep Neural Network (RAD2N)." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 10s (2023): 81–88. http://dx.doi.org/10.17762/ijritcc.v11i10s.7598.

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In metropolitan areas, the exponential growth in quantity of vehicles has instigated gridlock, pollution, and delays in the transportation of freight. IoT is the modern revolution which pushes the world towards intelligent management systems and automated procedures. This makes a significant contribution to automation and intelligent societies. Traffic regulation and effective congestion management assist conserve many priceless resources. In order to recognize, collect and send data, autonomous vehicles are furnished with IoT powered Intelligent Traffic Management System (ITMS) having a set of sensors. Moreover, machine learning (ML) algorithms can also be employed to enhance the transportation system. Traffic jams, delays, and a high death rate are the results of the problems that the current transport management systems face. In this paper, an active traffic control for VANET is proposed which merges Roaming Agents (RA) with deep neural networks (DNN). The effectiveness of the DNN with RA (RAD2N) routing method in VANETs is evaluated experimentally and compared with the traditional ML and other DL routing algorithms. Several traffic congestion indicators, including delay, packet delivery ratio (PDR) and throughput are used to validate RAD2N. The outcomes demonstrate that the proposed approach delivers lower latency and energy consumption.
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Harun, Nur Emilia binti Abdullah, Harun, Shamsulkahar, and Shahryar Sorooshian. "Impact Of Industrial 4.0 On Design Of Products And Services." Journal of Management and Science 8, no. 2 (2018): 165–69. http://dx.doi.org/10.26524/jms.2018.14.

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Industrial 4.0 is defined as the fourth industrial revolution. The wave of changes which includes interoperability, information transparency, technical assistance and decentralized decision. The industry waving into 4.0 that has been called the “smart factory” which computers and automation come together where robotics connected remotely to computer systems equipped with machine which can learning the algorithms to control the robotics with very little input from human operators. The companies must follow what trend the industry going to and reengineering the management in each department. Smart factory bring in the smart production would provide smart product and services. The wave of change in technology and physical process would impact the companies in kind of products and physical process which completely change in every steps of the way. Companies must catch up with the trends, demand and face the challenges upon the rapid growth of the industrial technology.
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Haider, Amir, Yiqiao Wei, Shuzhi Liu, and Seung-Hoon Hwang. "Pre- and Post-Processing Algorithms with Deep Learning Classifier for Wi-Fi Fingerprint-Based Indoor Positioning." Electronics 8, no. 2 (2019): 195. http://dx.doi.org/10.3390/electronics8020195.

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To accommodate the rapidly increasing demand for connected infrastructure, automation for industrial sites and building smart cities, the development of Internet of Things (IoT)-based solutions is considered one of the major trends in modern day industrial revolution. In particular, providing high precision indoor positioning services for such applications is a key challenge. Wi-Fi fingerprint-based indoor positioning systems have been adapted as promising candidates for such applications. The performance of such indoor positioning systems degrade drastically due to several impairments like noisy datasets, high variation in Wi-Fi signals over time, fading of Wi-Fi signals due to multipath propagation caused by hurdles, people walking in the area under consideration and the addition/removal of Wi-Fi access points (APs). In this paper, we propose data pre- and post-processing algorithms with deep learning classifiers for Wi-Fi fingerprint-based indoor positioning, in order to provide immunity against limitations in the database and the indoor environment. In addition, we investigate the performance of the proposed system through simulation as well as extensive experiments. The results demonstrate that the pre-processing algorithm can efficiently fill in the missing Wi-Fi received signal strength fingerprints in the database, resulting in a success rate of 88.96% in simulation and 86.61% in a real-time experiment. The post-processing algorithm can improve the results from 9.05–10.94% for the conducted experiments, providing the highest success rate of 95.94% with a precision of 4 m for Wi-Fi fingerprint-based indoor positioning.
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Villalpando-Hernandez, Rafaela, David Munoz-Rodriguez, and Cesar Vargas-Rosales. "Relational Positioning Method for 2D and 3D Ad Hoc Sensor Networks in Industry 4.0." Applied Sciences 11, no. 19 (2021): 8907. http://dx.doi.org/10.3390/app11198907.

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Industry 4.0, or smart factory, refers to the fourth industrial revolution, as a result of which it is expected that massive sensor deployment, high device connectivity, automation, and real-time data acquisition support improve industrial processes. Sensor fault detection and operator tracking allow adequate performance of the sensor network supporting Industry 4.0, and sensor localization has become crucial to enable sensor fault detection. Hence, the development of new localization methods is necessary for environments where GPS localization technology is unfeasible. Furthermore, position location information (PLI) is a crucial requirement for the deployment of multiple services and applications in wireless ad hoc and sensor networks supporting Industry 4.0. Three-dimensional (3D) scenarios are considered to extend the applicability of PLI-based services. However, PLI acquisition presents several challenges within any 3D ad hoc sensor network paradigm. For instance, conventional triangulation algorithms are often no longer applicable due to the lack of direct land-fixed references and the inclusion of a third-dimension coordinate. In this paper, a PL relational searching algorithm suitable for 2D and 3D ad hoc environments was formulated in terms of a discretized searching space and a vertex weight assignment process according to distance relationships from anchor nodes to the node to be located on a feasibility space. The applicability of the proposed algorithm was examined through analytic formulation and simulation processes. Results show small distance errors (less than 1 m) are achievable for some scenarios.
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Dr. Rahul Pulimamidi, Prabu Ravichandran. "Enhancing Healthcare Delivery: AI Applications In Remote Patient Monitoring." Tuijin Jishu/Journal of Propulsion Technology 44, no. 3 (2023): 3948–54. http://dx.doi.org/10.52783/tjjpt.v44.i3.2160.

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Research in Remote Patient Monitoring Systems (RPMS) is highly valued since it directly impacts people's health and wellbeing. The number of cases being treated with RPMS has increased since the beginning of the pandemic. However, there are still challenges to be met, such as mobility, heterogeneous networks, standardisation of RPMSs, automation, and Quality of Service (QoS), despite the rise in their use. The incorporation of artificial intelligence (AI) into remote patient monitoring (RPM) is causing a revolution in the healthcare industry by improving the quality of care provided to patients, boosting operational effectiveness, and making it possible to intervene earlier. RPM makes use of technology to monitor the health of patients in a remote location, hence minimising the frequency of in-person visits that are required. The addition of AI algorithms further enhances the potential of RPM by analysing huge volumes of patient data to identify patterns, abnormalities, and potential problems. In this study, we will investigate the most potentially game-changing applications of AI in remote patient monitoring in 2023 and beyond.
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Temitayo Oluwaseun Jejeniwa, Noluthando Zamanjomane Mhlongo, and Titilola Olaide Jejeniwa. "A COMPREHENSIVE REVIEW OF THE IMPACT OF ARTIFICIAL INTELLIGENCE ON MODERN ACCOUNTING PRACTICES AND FINANCIAL REPORTING." Computer Science & IT Research Journal 5, no. 4 (2024): 1031–47. http://dx.doi.org/10.51594/csitrj.v5i4.1086.

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The rapid integration of artificial intelligence (AI) into various industries has catalyzed transformative changes in accounting practices and financial reporting. This comprehensive review explores the multifaceted impact of AI on modern accounting, shedding light on the ways in which advanced technologies are reshaping traditional financial processes. The implementation of AI in accounting has led to increased efficiency and accuracy in routine tasks. Automation of data entry, reconciliation, and routine bookkeeping activities has not only reduced the risk of human errors but has also allowed accountants to redirect their focus towards more strategic and value-added activities. Machine learning algorithms are adept at analyzing vast datasets, identifying patterns, and predicting financial trends, enabling accountants to make more informed decisions. Furthermore, AI has revolutionized the auditing process, enhancing the detection of anomalies and fraudulent activities. Through continuous monitoring and analysis of financial data, AI-powered systems can quickly identify discrepancies, mitigating risks and ensuring the integrity of financial reports. This has profound implications for regulatory compliance and corporate governance, fostering greater transparency and accountability. In the realm of financial reporting, AI has played a pivotal role in improving the quality and timeliness of information. Natural Language Processing (NLP) technologies enable the extraction of valuable insights from unstructured data sources, facilitating the generation of comprehensive and insightful financial reports. This not only accelerates the reporting process but also enhances the communicative value of financial information to stakeholders. Despite the evident benefits, the widespread adoption of AI in accounting brings forth challenges such as ethical considerations, data security, and the need for upskilling the workforce. Ethical concerns regarding bias in AI algorithms and the responsible use of automation in decision-making processes necessitate a thoughtful approach towards AI integration in accounting practices. In conclusion, this review underscores the transformative impact of AI on modern accounting practices and financial reporting. As organizations navigate this technological revolution, a balanced approach that addresses ethical concerns while maximizing the benefits of AI will be crucial for the continued evolution of the accounting profession. Keywords: Impact, Artificial Intelligence, Modern, Accounting, Practices.
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Campos, Thainá Loise Grangeiro. "DIREITO, NOVAS TECNOLOGIAS E O BIG DATA: DESAFIOS ÉTICOS NA UTILIZAÇÃO DA INTELIGÊNCIA ARTIFICIAL." Revista ft 29, no. 144 (2025): 58–59. https://doi.org/10.69849/revistaft/ni10202503221458.

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Artificial Intelligence (AI) has emerged as a transformative force in contemporary society, permeating various aspects of daily life and playing a crucial role in the so-called "Fourth Industrial Revolution." This revolution, characterized by the fusion of digital, physical, and biological technologies, promises to reshape the way we live, work, and interact. However, this rapid technological acceleration is not without significant challenges and concerns. The swift expansion of AI carries the potential to infringe upon fundamental rights, exacerbate social inequalities, and perpetuate biases.Big Data refers to the vast volume and diversity of data collected, stored, and processed by public and private entities, posing a significant risk of violating the fundamental right to data protection, which was incorporated into the Brazilian Federal Constitution through Constitutional Amendment No. 115/2022 and is enshrined in Article 5, item LXXIX. Furthermore, the interaction between artificial intelligence, Big Data, and algorithms reveals a tendency toward discriminatory biases that influence critical decisions, from credit systems to criminal justice. The lack of adequate regulation may result in discrimination and injustices, compromising individual rights and exacerbating preexisting disparities.It is imperative that the legal system keeps pace with this technological evolution by establishing robust ethical standards that ensure the fair and equitable application of AI. In the legal field, AI is increasingly used, from contract drafting automation to assisting in complex judicial decisions. However, the implementation of these technologies must be accompanied by stringent transparency and accountability mechanisms, particularly within the criminal justice system, where errors can have devastating consequences for individuals and communities. To mitigate these challenges, a continuous dialogue among legislators, legal professionals, and technology experts is essential to develop policies that protect fundamental rights, promote justice, and ensure fairness. Ethics must be at the core of these discussions, ensuring that AI algorithms are designed and deployed in a manner that prevents discrimination and upholds universal human rights principles.
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Barsegyan, N. V., and R. R. Zaripova. "METHODOLOGICAL APPROACHES TO THE STUDY OF THE EFFECTIVENESS OF THE IMPLEMENTATION OF THE INDUSTRY 4.0 CONCEPT." Izvestiya of Samara Scientific Center of the Russian Academy of Sciences 23, no. 6 (2021): 47–51. http://dx.doi.org/10.37313/1990-5378-2021-23-6-47-51.

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The need to promptly identify problems with the simultaneous production of the necessary corrective actions, which are aimed at optimizing the productivity of the entire production system, justifies the relevance of the study of the effectiveness of the introduction of industry 4.0 technologies in industry. The article provides a review of analytical studies of the concept of "industry 4.0" in foreign scientific literature to identify characteristics and distinguish from related concepts, such as "4th Industrial Revolution", "cyberphysical systems", "Internet of Things". The main key factors for the success of digitalization are people, strategy, technologies that will allow the development of intelligent algorithms to improve operational efficiency; contribute to the efficient use of resources, reduce product quality costs, introduce automation; contribute to increasing the efficiency and productivity of operations. The main directions of the impact of Industry 4.0 technologies proposed by the Boston Consulting Company on the efficiency of production systems are presented. It is concluded that the use of digital technologies in industry 4.0 makes it possible for large-scale technological transformation of production and is aimed at increasing the innovative potential of industrial enterprises.
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Dr. Prakash Upadhyay, Tushar Sharma, and Ahmadi Fatima. "Impact of AI-Driven Digital Twins in Industry 4.0: an Exploratory Analysis." International Research Journal on Advanced Engineering and Management (IRJAEM) 2, no. 05 (2024): 1548–57. http://dx.doi.org/10.47392/irjaem.2024.0210.

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Human society is witnessing a revolutionary growth of digital twin (DT) and artificial intelligence (AI) technologies, which has greater impact on Industry 4.0 revolution specially in academia and industry. DT is a digital representation of a physical entity, with data and infrastructure serving as its foundation, algorithms, and models as its core, and software and services as its application. The methodical and thorough integration of domain-specific expertise is even more essential to the foundations of DT and AI in industrial sectors. This paper provides a thorough analysis of more than 30 articles on AI-driven DT technologies employed in Industry 4.0 over the previous five years. It also describes the general advances of these technologies and the current status of AI integration in the domains of advanced robotics and smart manufacturing which are affecting human society. These include established methods like industrial automation as well as complex mechanism like 3D printing and human-robot collaboration. Additionally, the benefits of AI-powered DTs are explained in relation to sustainable development. The development potential and practical difficulties of AI-driven DTs are examined, with varying emphasis on various levels.
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Khodabakhshian, Ania, Taija Puolitaival, and Linda Kestle. "Deterministic and Probabilistic Risk Management Approaches in Construction Projects: A Systematic Literature Review and Comparative Analysis." Buildings 13, no. 5 (2023): 1312. http://dx.doi.org/10.3390/buildings13051312.

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Risks and uncertainties are inevitable in construction projects and can drastically change the expected outcome, negatively impacting the project’s success. However, risk management (RM) is still conducted in a manual, largely ineffective, and experience-based fashion, hindering automation and knowledge transfer in projects. The construction industry is benefitting from the recent Industry 4.0 revolution and the advancements in data science branches, such as artificial intelligence (AI), for the digitalization and optimization of processes. Data-driven methods, e.g., AI and machine learning algorithms, Bayesian inference, and fuzzy logic, are being widely explored as possible solutions to RM domain shortcomings. These methods use deterministic or probabilistic risk reasoning approaches, the first of which proposes a fixed predicted value, and the latter embraces the notion of uncertainty, causal dependencies, and inferences between variables affecting projects’ risk in the predicted value. This research used a systematic literature review method with the objective of investigating and comparatively analyzing the main deterministic and probabilistic methods applied to construction RM in respect of scope, primary applications, advantages, disadvantages, limitations, and proven accuracy. The findings established recommendations for optimum AI-based frameworks for different management levels—enterprise, project, and operational—for large or small data sets.
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Guendouz, Tarek. "Artificial Intelligence-Powered Customer Experience Management (Moving from Mass to Hyper-Personalization in light of Relationship Marketing)." International Journal for Scientific Research 3, no. 6 (2024): 247–306. http://dx.doi.org/10.59992/ijsr.2024.v3n6p9.

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This study aims to shed light on the phenomenon of Customer Experience (CX) enhanced by Artificial Intelligence (AI) as a Sustainable Competitive Advantage (SCA) for the relationship marketing industry in general, and Customer Relationship Management (CRM) in particular. It takes into account the great aura of accelerated scientific advancement and accumulated cognitive development on the one hand, and the technical and cyber progress that are expanding at a very rapid pace day after day, on the other hand. These two considerations led to a huge economic revolution that have had a profound impact on all sectors and at various levels. The industrial world moved from the Fourth Industrial Revolution (Industry 4.0) based on Cyber-Physical Systems (CPS), to the Fifth Industrial Revolution (Industry 5.0) which centers on humans and collaborative robots (Cobot). AI algorithms are among the most important outcomes produced by the digital and cloud environment supported by Information and Communications Technology (ICT) and Internet networks (Web 3.0; Web 4.0). These innovative mechanisms revealed their superior and enormous ability as well as their astonishing capability to change the rules of the competitive game and modify the laws of marketing and forces of supply and demand. This theoretical study found that CX personalization is a vital and rich source for the establishing strategic success and sustainable excellence in target markets and managing to overcome and outperform current and potential competitors. It is also considered one of the most important modern marketing approaches that has benefited from: Automation Solutions, Machine Learning (ML), Natural Language Processing (NLP), Large Language Models (LLMs), Generative AI, Big Data Analysis, Internet of Things (IoT) Sensors, Blockchain, Augmented Reality (AR), Virtual Reality (VR), Cloud Computing (CC), and Quantum Computing (QC), ..., etc. The consequences of harnessing and adapting these efficient and effective tools and means to achieve an exceptional performance expected for brands are based on improving and enhancing the CX through Hyper-Customization instead of Mass Customization.
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Wang, Meng. "Intelligent Malfunction Identification Method in Mechanical Manufacturing Process Based on Multisensor Data." Discrete Dynamics in Nature and Society 2022 (May 23, 2022): 1–9. http://dx.doi.org/10.1155/2022/6720166.

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Current technology trends have been gradually integrated into the production of all walks of life, which play an indispensable part in promoting the intelligent development of enterprises, and have brought a greater impact on production and reformation. With the rapid development of the economy and technology, the manufacturing industry has played a very important role. For this reason, the introduction of artificial intelligence into machinery manufacturing can not only improve production efficiency but also save labor and reduce labor costs. The application of artificial intelligence in machinery manufacturing has a critical good role in promoting industrial upgrading and transformation. This time, through the application of smart algorithms in machinery manufacturing and its automation, we expect that such a technological revolution can provide a new development prospect for the development of manufacturing intelligence and automation. Taking the malfunction identification of string striking machinery as an example, this paper studies the smart identification method of mechanical malfunction based on multisensor. In the process of malfunction identification of keyboard stroke machinery, the accuracy of malfunction identification results is low due to the influence of the identification model. Moreover, a malfunction identification and analysis method for keyboard stroke machinery based on BP optimized by GA is proposed. The mechanical data of keyboard chords are acquired by sound-sensitive sensors, and the data features are extracted by wavelet packet decomposition. Based on the optimized BP, a mechanical malfunction judgment model is constructed, and various parameters in the model are calculated. The results show that the intelligent identification method proposed has exhibited strong adaptability and superiority compared with the traditional method.
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Saniuk, Sebastian, and Sandra Grabowska. "Knowledge and Skills Development for Implementing the Industry 5.0 Concept." European Conference on Knowledge Management 24, no. 2 (2023): 1188–95. http://dx.doi.org/10.34190/eckm.24.2.1329.

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The dehumanisation of the industry due to focusing only on the implementation of Industry 4.0 technologies has resulted in numerous concerns among workers, governments and societies regarding new working conditions and the role of humans in industry and the economy. Hence, the European Commission proposed the new concept of Industry 5.0. Industry 5.0 complements and extends the characteristic features of Industry 4.0. It highlights aspects that will be decisive factors in industry placement in future European society. Numerous scientific studies indicate the need to take into account, in the assumptions of the future industry's development, the crucial role of human beings. The humanisation of the technological Industry 4.0 environment was one of the first factors in the evolution of Industry 4.0 towards the Industry 5.0 concept. The new approach in the fourth industrial revolution focuses on the interaction between humans and intelligent machines. The fifth industrial revolution will continue the push for more advanced human-machine interfaces using artificial intelligence (AI) algorithms. It will mean better integration, enabling faster, better automation combined with the power of human brains, but it will also mean changing the demands placed on managers and engineers. Hence, the article aims to identify the critical knowledge and skills of engineers responsible for implementing the Industry 5.0 concept. The presented achievements and results in the article are from surveys conducted among experts representing companies with experience in implementing Industry 4.0 technologies and with a high level of knowledge and engineering and managerial competencies. The research results presented in the paper are dedicated to researchers and practitioners implementing the Industry 5.0 concept in smart organisations (smart factories).
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Dr.A.Shaji, George. "Robo-Revolution: Exploring the Rise of Automated Financial Advising Systems and Their Impacts on Management Practices." Partners Universal Multidisciplinary Research Journal (PUMRJ) 01, no. 04 (2024): 1–16. https://doi.org/10.5281/zenodo.14059485.

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Robo-advisors, the automated financial advisory services leveraging algorithmic and data-driven methodologies for portfolio management, have experienced incredible growth over the past ten years. This paper investigates the technical capabilities and increasing adoption of robo-advisory systems spanning many domains. Global assets under automated management expected to approach $12 trillion by 2025 will transform practices in wealth management, institutional investment, operations analytics, and strategic decision support under Robo-advisors. An overview examines the core functions of machine learning, data science and natural language processing that enable robo-advisors to deliver customized guidance and executable actions with increasing sophistication. The current landscape covers leading platforms demonstrating rapid scaling and new specializations by financial sector. Most research focuses on expected and developing consequences on financial and related spheres of management priorities. Automated advisers are upsetting accepted methods of portfolio balance and risk modeling, which calls both managerial operational changes and mental adjustments. As the systems advance, they may profoundly alter practices around goal setting, long-term planning, regulatory adherence, and transparency expectations. Additionally, case studies suggest robo-utilization for tactical tasks is freeing management bandwidth for more strategic, values-based decision making. By handling time-intensive profiling, monitoring, and reporting, automated advisors grant institutions greater capacities in governance, relationship-building and innovation. The study uses case studies from real estate brokers, supply chain coordinators, investment organizations, and wealth corporations.  Finally, suggestions for managers to use robo-advisor technology for improved analytics, foresight, and competitive positioning are given together with control to match ethical criteria and community interests. Maintaining human checks and balances becomes essential as algorithms become increasingly common in banking. Charting this balance will help us to guide responsible progress.
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Kirichenko, N. "The ideology of information and computer technology as a vector of development of intellectual resources in a digital society." Fundamental and applied researches in practice of leading scientific schools 31, no. 1 (2019): 84–88. http://dx.doi.org/10.33531/farplss.2019.1.18.

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The relevance of the study of this problem is that information and computer technologies contribute to the development of digital society, based on the development of human resources that are intellectual capital. Information and computer technology affect the development of machines that replaced people and gave rise to "technological unemployment." The purpose of the study is to show how the information revolution of the twenty-first century contributes to the reduction of labor as a result of progressive robotization. The technologies that are used today to replace people are different; the need for human resources is reduced thanks to robots, computers and other high-tech gadgets. Methods of theoretical analysis - deduction and induction, historical and logical, comparative and structural-genetic analysis, information method, which contribute to the insight into the essence of the phenomenon under study as a complex phenomenon and dynamic process. Results: It has been proven that, thanks to various well-known developments in information-computer technologies and robotics, many experts believe that society is at an early stage of the new industrial (post-industrial) revolution, which in the future can change the way people live and work just like 200 years ago made a steam engine. Technological unemployment is one of the main reasons for the increase in the overall unemployment rate in Western countries over the past 30 years. Although to some extent this is due to the demographic revolution and the changing structure of the economy in many countries, the development of information and computer technologies, as well as other types of automation and the Internet have played a significant role, especially since 2000. Findings. We have shown that many jobs with cheap labor can disappear, because the digital society focuses on the development of human (intellectual) resources. The world is turning into a digital society and the world is ruled by a figure based on intelligence, intelligence, algorithms, digitalization. The digital society consists of a set of algorithms that are controlled by information and computer technologies that penetrate digital management, which is based on intellectual-rational force represented by human resources. It is human resources that develop robotics, artificial intelligence, computerization, mechanization, robotization, which are based on robotics, artificial intelligence. These varieties of digital society will accelerate the potential for long-term productivity gains through intellectualization. Practical recommendations - to develop a small business that rests on the network of intelligent platforms, in connection with which to create jobs on the Internet and create new types of employment.
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Kaaria, Ann Gaceri. "Artificial Intelligence and Employee Well-Being: Balancing Technological Progressions with Human-Centric Workplace Strategies, a Research Agenda." East African Journal of Information Technology 7, no. 1 (2024): 355–65. http://dx.doi.org/10.37284/eajit.7.1.2281.

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Artificial intelligence (AI) enabled technologies are now corporate organisations' top priorities due to the availability of large data and the advent of the Internet of Things during the past ten years. AI is becoming a crucial component of business model innovation, process transformation, disruption, and gaining a competitive edge for companies adopting digital and data-centric cultures. This study investigates the implications of smart technology, artificial intelligence, robotics, and algorithms (STARA) on the future of work, with a particular emphasis on employee well-being and workplace dynamics. As futurists project that by 2025, 52% of all work functions will be automated, replacing one-third of current jobs, the rapid advancement of STARA creates both opportunities and risks. While automation has the potential to produce 133 million new jobs, it also threatens to eliminate 75 million employments, raising employee anxieties about job security and future roles. Despite the rising volume of studies on smart automation, there is still a major vacuum in our understanding of its implications on employees' mental health, well-being, and the entire workplace. This study investigates STARA's dual influence: while technology reduces physical strain and automates tedious jobs, it also raises new difficulties such as job displacement concerns and shifts in worker dynamics. The study emphasizes the need of human resource professionals to develop methods that strike a balance between technological integration and employee assistance. Key areas of focus include providing reskilling opportunities, adopting mental health initiatives, and encouraging open conversation regarding AI's expanding role in the workforce. By addressing these concerns, organisations may build a more resilient workforce that is better suited to the fourth industrial revolution. The study intends to contribute to a better understanding of how organisations may safeguard and improve employee well-being in the face of fast technological change, ensuring that STARA integration encourages innovation while simultaneously supporting a healthy and engaged workforce
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Nkwede, Maria, and Chukwuma Aniuga. "Artificial Intelligence: Challenges and Opportunities for the Accounting Profession in Nigeria." African Journal of Politics and Administrative Studies 16, no. 1 (2023): 1–17. http://dx.doi.org/10.4314/ajpas.v16i1.1.

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Artificial intelligence with its fifth industrial revolution is fast griping the African continent. The emergence and adoption of artificial intelligence applications and systems is fast becoming a normal trend in emerging markets landscape across Africa. This paper is borne out of the need to identify challenges that professional grapple with in their quest to surmount the challenges posed by the realities of artificial intelligence applications in Africa. The study adopted the thematic style in presenting the challenges as well as the prospects of artificial intelligence applications for the accounting professional in emerging market. Some of the challenges of artificial intelligence application identified in this study include: complex algorithms, reduced investment, and software failure, lack of political will and limitations amongst others. On the other hand, opportunities of artificial intelligence in emerging market include transportation automation, technological cyborgs and robotic companions amongst others. From the findings, accounting professional are advised to strive harder in order to beat competition by delivering quality services to her clients through harnessing opportunities for rebranding, reengineering and radically improving the business and investment decisions which is the ultimate purpose of the profession. More so, they are encouraged to develop a novel set of proficiency revolving around data in the profession
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Singh, Omprakash, Vishnu Kumar Barodiya, Abhishek Paridwal, and Aman Makhija. "Reinforcement Learning in Robotics: Challenges and Applications." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 11, no. 2 (2020): 743–45. http://dx.doi.org/10.61841/turcomat.v11i2.14417.

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Reinforcement Learning (RL) is at the vanguard of robotics revolution, allowing machines to learn and make choices in complicated environments. This paper explores the symbiotic dating between RL and robotics, focusing at the challenges and programs that shape this interdisciplinary area. The dialogue makes a speciality of the fundamental standards of RL and its integration into robotics, elucidating the specific demanding situations encountered on this merger. The research covers the space among simulation-primarily based schooling and real-world applicability, navigating hardware obstacles, and addressing safety concerns, organising a comprehensive view of the challenges encountered when deploying RL for robotic systems. This paper also offers insights into the various programs of RL in robotics, inclusive of self sustaining navigation, item manipulation, healthcare, and business automation. It investigates how RL algorithms assist robots navigate complex environments, gain item manipulation dexterity, and contribute to healthcare improvements and industrial optimization. Case studies exhibit the software of RL in robotics by means of highlighting a hit implementations and demonstrating the transformative capability of this aggregate. Finally, this paper outlines future instructions, paving the way for persisted innovation and emphasizing the importance of bridging the theoretical advancements and real-international deployment in RL-driven robotics.
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Popartan, Lucia Alexandra, Àtia Cortés, Manel Garrido-Baserba, Marta Verdaguer, Manel Poch, and Karina Gibert. "The Digital Revolution in the Urban Water Cycle and Its Ethical–Political Implications: A Critical Perspective." Applied Sciences 12, no. 5 (2022): 2511. http://dx.doi.org/10.3390/app12052511.

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The development and application of new forms of automation and monitoring, data mining, and the use of AI data sources and knowledge management tools in the water sector has been compared to a ‘digital revolution’. The state-of-the-art literature has analysed this transformation from predominantly technical and positive perspectives, emphasising the benefits of digitalisation in the water sector. Meanwhile, there is a conspicuous lack of critical literature on this topic. To bridge this gap, the paper advances a critical overview of the state-of-the art scholarship on water digitalisation, looking at the sociopolitical and ethical concerns these technologies generate. We did this by analysing relevant AI applications at each of the three levels of the UWC: technical, operational, and sociopolitical. By drawing on the precepts of urban political ecology, we propose a hydrosocial approach to the so-called ‘digital water ‘, which aims to overcome the one-sidedness of the technocratic and/or positive approaches to this issue. Thus, the contribution of this article is a new theoretical framework which can be operationalised in order to analyse the ethical–political implications of the deployment of AI in urban water management. From the overview of opportunities and concerns presented in this paper, it emerges that a hydrosocial approach to digital water management is timely and necessary. The proposed framework envisions AI as a force in the service of the human right to water, the implementation of which needs to be (1) critical, in that it takes into consideration gender, race, class, and other sources of discrimination and orients algorithms according to key principles and values; (2) democratic and participatory, i.e., it combines a concern for efficiency with sensitivity to issues of fairness or justice; and (3) interdisciplinary, meaning that it integrates social sciences and natural sciences from the outset in all applications.
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ALORDIAH, CAROLINE OCHUKO. "APPRECIATING THE AI REVOLUTION: EMPOWERING EDUCATIONAL RESEARCHERS THROUGH AI TOOLS FOR WRITING RESEARCH ARTICLES." Zamfara International Journal of Humanities 2, no. 01 (2023): 178–95. http://dx.doi.org/10.36349/zamijoh.2023.v02i01.013.

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The use of AI-based technologies in the process of researching and writing up findings has garnered a lot of attention in the world of education. This research investigates the use of AI techniques in the field of educational research as well as the repercussions of doing so. The purpose of this article is to offer advice and suggestions to help researchers navigate the changing environment of research writing in the age of artificial intelligence (AI). The advantages of using AI tools are investigated in this study. Some of these benefits include higher research efficiency, enhanced research quality, expanded accessibility, and chances for skill development. Additionally, it covers ethical concerns such as the privacy of data and the biases introduced by algorithms. In addition, the research highlights the need of striking a balance between the automation that can be achieved through AI and the knowledge that can only be obtained from humans. This means ensuring that AI tools supplement critical writing abilities rather than replacing them. The study suggests that researchers become familiar with AI tools, evaluate the capabilities of such tools, and choose the tools that are the most fit for certain research objectives. This will help researchers fill the shortcomings that were observed. In addition to this, it recommends the formulation of standards for the ethical use of AI, as well as the encouragement of activities that build skills, such as critical thinking and academic writing, to be practiced concurrently with the utilization of AI technologies. In addition, the research highlights the significance of promoting cooperation and the exchange of information within the research community, advocating for accessibility and fairness, and continually analyzing and adjusting to improvements in artificial intelligence technology. Educational researchers may successfully exploit artificial intelligence (AI) technologies in research writing, improve research methods, and contribute to the growth of the field if they adopt the recommendations offered in this study and implement them.
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Venkatesan, SaravanaKumar, Jonghyun Lim, Hoon Ko, and Yongyun Cho. "A Machine Learning Based Model for Energy Usage Peak Prediction in Smart Farms." Electronics 11, no. 2 (2022): 218. http://dx.doi.org/10.3390/electronics11020218.

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Context: Energy utilization is one of the most closely related factors affecting many areas of the smart farm, plant growth, crop production, device automation, and energy supply to the same degree. Recently, 4th industrial revolution technologies such as IoT, artificial intelligence, and big data have been widely used in smart farm environments to efficiently use energy and control smart farms’ conditions. In particular, machine learning technologies with big data analysis are actively used as one of the most potent prediction methods supporting energy use in the smart farm. Purpose: This study proposes a machine learning-based prediction model for peak energy use by analyzing energy-related data collected from various environmental and growth devices in a smart paprika farm of the Jeonnam Agricultural Research and Extension Service in South Korea between 2019 and 2021. Scientific method: To find out the most optimized prediction model, comparative evaluation tests are performed using representative ML algorithms such as artificial neural network, support vector regression, random forest, K-nearest neighbors, extreme gradient boosting and gradient boosting machine, and time series algorithm ARIMA with binary classification for a different number of input features. Validate: This article can provide an effective and viable way for smart farm managers or greenhouse farmers who can better manage the problem of agricultural energy economically and environmentally. Therefore, we hope that the recommended ML method will help improve the smart farm’s energy use or their energy policies in various fields related to agricultural energy. Conclusion: The seven performance metrics including R-squared, root mean squared error, and mean absolute error, are associated with these two algorithms. It is concluded that the RF-based model is more successful than in the pre-others diction accuracy of 92%. Therefore, the proposed model may be contributed to the development of various applications for environment energy usage in a smart farm, such as a notification service for energy usage peak time or an energy usage control for each device.
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Choi, So-Won, Eul-Bum Lee, and Jong-Hyun Kim. "The Engineering Machine-Learning Automation Platform (EMAP): A Big-Data-Driven AI Tool for Contractors’ Sustainable Management Solutions for Plant Projects." Sustainability 13, no. 18 (2021): 10384. http://dx.doi.org/10.3390/su131810384.

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Plant projects, referred to as Engineering Procurement and Construction (EPC), generate massive amounts of data throughout their life cycle, from the planning stages to the operation and maintenance (OM) stages. Many EPC contractors struggle with their projects due to the complexity of the decision-making processes, owing to the vast amount of project data generated during each project stage. In line with the fourth industrial revolution, the demand for engineering project management solutions to apply artificial intelligence (AI) in big data technology is increasing. The purpose of this study was to predict the risk of contractor and support decision-making at each project stage using machine-learning (ML) technology based on data generated in the bidding, engineering, construction, and OM stages of EPC projects. As a result of this study, the Engineering Machine-learning Automation Platform (EMAP), a cloud-based integrated analysis tool applied with big data and AI/ML technology, was developed. EMAP is an intelligent decision support system that consists of five modules: Invitation to Bid (ITB) Analysis, Design Cost Estimation, Design Error Checking, Change Order Forecasting, and Equipment Predictive Maintenance, using advanced AI/ML algorithms. In addition, each module was validated through case studies to assure the performance and accuracy of the module. This study contributes to the strengthening of the risk response for each stage of the EPC project, especially preventing errors by the project managers, and improving their work accuracy. Project risk management using AI/ML breaks away from the existing risk management practices centered on statistical analysis, and further expands the research scalability of related works.
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John, Awodola Olufemi, and Babatunde Bamidele Oyeyemi. "The Role of AI in Oil and Gas Supply Chain Optimization." International Journal of Multidisciplinary Research and Growth Evaluation 3, no. 1 (2022): 1075–86. https://doi.org/10.54660/.ijmrge.2022.3.1.1075-1086.

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The oil and gas industry is undergoing a revolution in supply chain management through the integration of artificial intelligence (AI) technologies, such as automation, machine learning, and predictive analytics, which are being used more and more to optimize various aspects of the oil and gas supply chain, from exploration and production to transportation and distribution. AI-driven solutions allow for real-time monitoring and data analysis, which enables businesses to predict equipment failures, optimize logistics, and improve the accuracy of demand forecasting. In exploration and production, AI is used to analyze vast amounts of seismic and geological data, which speeds up the identification of possible drilling sites and improves the efficiency of resource extraction. By using machine learning algorithms to forecast reservoir behavior, operators can minimize operational risks and improve drilling methods. AI also plays a key role in pipeline monitoring and maintenance, utilizing sensor data to identify irregularities, reduce downtime, and avert expensive mishaps. AI improves scheduling and routing in distribution and transportation to speed up deliveries and use less fuel. AI-based solutions can also assist with inventory control, guaranteeing the effective distribution of gas and oil supplies throughout international markets. However, despite its enormous potential, the oil and gas industry faces obstacles like data integration, cybersecurity threats, and the need for skilled labor. As AI technologies continue to advance, their role in improving the sustainability and efficiency of oil and gas supply chains will only grow more prominent, leading to more streamlined operations and a reduction in environmental impact. Predictive maintenance powered by AI algorithms helps to prevent delays by identifying potential issues before they lead to costly disruptions.
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Abrar, Fatima, Rameez Ahmed, Ikhtiar Ahmed Khoso, Nadeem Farooq, and Ikramullah Ibrahimi. "HR 4.0 in Manufacturing: Using AI and Big Data to Enhance Workforce Agility and Reduce High Employee Turnover Intentions." Research Journal for Social Affairs 3, no. 1 (2025): 261–70. https://doi.org/10.71317/rjsa.003.01.0078.

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The human resource management field in manufacturing operations experienced major transformations because of Industrial Revolution 4.0 (IR 4.0) with HR 4.0 as its main advancement combining artificial intelligence systems with big data analytics. The merged system has built organizational employee flexibility and automated HR procedures leading to reduced employee departures. Through the integration of AI in HRM organizations have gained the ability to analyze advanced predictive data alongside improved employee engagement and optimized talent control which has boosted their HR process automation capability. The research evaluates how the implementation of AI algorithms for HR management impacts employee retention outcomes through big data examination. Secondary statistical research of previously collected data examines the part AI technology plays in enhancing employee retention and minimizing staff departures. The paper applies predictive models based on AI algorithms and big data which predict turnover risks. The predictive models assist organizations in detecting risky staff members and facilitate early prevention plans. To evaluate the relationship between AI-powered HR practices and turnover intentions the researchers have applied correlation methods together with regression analysis of Variance (ANOVA) and chi-square testing. The research employs statistical analysis to assess how AI-based solutions impact employee satisfaction levels and efficiency as well as decrease employee turnover rates. The implementation of AI and big data analytics succeeds in both launching employee retention programs effectively and increasing organizational efficiency through improved HR management better decision systems and adaptable workforce models that match Industry 4.0 manufacturing sector requirements. Organizations use these methods to create highly engaged and efficient staff who maintain stability which leads to better productivity and decreased expenses from employee replacement and new hire recruitment.
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Gravett, Willem Hendrik. "Is the Dawn of the Robot Lawyer upon us? The Fourth Industrial Revolution and the Future of Lawyers." Potchefstroom Electronic Law Journal 23 (June 15, 2020): 1–37. http://dx.doi.org/10.17159/1727-3781/2020/v23i0a6794.

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The practice of law has been largely shielded from technological developments in the course of the past 50 years. While the ways in which legal professionals process and share information have evolved with new technologies — primarily with the emergence of personal computers, email and the internet — these technologies have not fundamentally transformed it. However, if media reports are to be believed, advances in technology in general — and the field known as "Artificial Intelligence" (AI) in particular — are on lawyers' doorsteps, and the legal industry is on the cusp of radical change. Fuelled by big data, increased computing power and more effective algorithms, AI has the potential to fundamentally transform the way in which legal work is done, the way in which law firms conduct business, and the way in which lawyers deal with clients. A number of technologies that fall under the AI umbrella, such as machine learning, natural language processing, deep learning and others, have already brought about the automation of many tasks that were, until recently, performed exclusively by humans because they required human intelligence. AI systems can also be used to perform many tasks that lawyers routinely perform, such as contract analysis, case prediction and e-discovery. And, according to proponents, these emerging technologies can do it cheaper, faster and more efficiently. This contribution examines the notion that recent advances in technology will "disrupt" the legal profession. It first describes the astonishing advances in technological progress, especially the recent rise of AI. It then considers the technologies and areas of legal practice most susceptible to this disruption. It concludes by envisaging what AI might mean for the legal profession, and how current technological trends might, in a relatively short period of time, transform the way in which legal services are delivered.
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Angelopoulos, Angelos, Emmanouel T. Michailidis, Nikolaos Nomikos, et al. "Tackling Faults in the Industry 4.0 Era—A Survey of Machine-Learning Solutions and Key Aspects." Sensors 20, no. 1 (2019): 109. http://dx.doi.org/10.3390/s20010109.

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The recent advancements in the fields of artificial intelligence (AI) and machine learning (ML) have affected several research fields, leading to improvements that could not have been possible with conventional optimization techniques. Among the sectors where AI/ML enables a plethora of opportunities, industrial manufacturing can expect significant gains from the increased process automation. At the same time, the introduction of the Industrial Internet of Things (IIoT), providing improved wireless connectivity for real-time manufacturing data collection and processing, has resulted in the culmination of the fourth industrial revolution, also known as Industry 4.0. In this survey, we focus on the vital processes of fault detection, prediction and prevention in Industry 4.0 and present recent developments in ML-based solutions. We start by examining various proposed cloud/fog/edge architectures, highlighting their importance for acquiring manufacturing data in order to train the ML algorithms. In addition, as faults might also occur from sources beyond machine degradation, the potential of ML in safeguarding cyber-security is thoroughly discussed. Moreover, a major concern in the Industry 4.0 ecosystem is the role of human operators and workers. Towards this end, a detailed overview of ML-based human–machine interaction techniques is provided, allowing humans to be in-the-loop of the manufacturing processes in a symbiotic manner with minimal errors. Finally, open issues in these relevant fields are given, stimulating further research.
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